Improving the Accuracy and Transparency of Underwriting with AI to Transform the Life Insurance Industry

  • Marc Maier
  • Hayley Carlotto
  • Sara Saperstein
  • Freddie Sanchez
  • Sherriff Balogun
  • Sears Merritt

Abstract

Life insurance provides trillions of dollars of financial security for hundreds of millions of individuals and fami­lies worldwide. To simultaneously offer affordable products while managing this financial ecosystem, life-insurance companies use an underwriting process to assess the mortality risk posed by individual applicants. Traditional underwriting is largely based on examining an applicant’s health and behavioral profile. This manual process is incompatible with expectations of a rapid customer experience through digital capabilities. Fortunately, the availability of large historical data sets and the emergence of new data sources provide an unprecedented opportunity for artificial intelligence to transform under­writing in the life-insurance industry with standard measures of mortality risk. We combined one of the largest application data sets in the industry with a responsible artificial intelligence framework to develop a mortality model and life score. We describe how the life score serves as the primary risk-driving engine of deployed algorithmic underwriting systems and demonstrate its high level of accuracy, yielding a nine-percent reduction in claims within the healthiest pool of applicants. Additionally, we argue that, by embracing transparency, the industry can build consumer trust and respond to a dynamic regulatory environment focused on algorithmic decision-making. We present a consumer-facing tool that uses a state-of-the-art method for interpretable machine learning to offer transparency into the life score.

Published
2020-09-14
How to Cite
Maier, M., Carlotto, H., Saperstein, S., Sanchez, F., Balogun, S., & Merritt, S. (2020). Improving the Accuracy and Transparency of Underwriting with AI to Transform the Life Insurance Industry. AI Magazine, 41(3), 78-93. https://doi.org/10.1609/aimag.v41i3.5320
Section
Special Topic Articles